Digital financial risk assessment method based on machine learning algorithm

In order to explore more accurate and efficient means of digital financial risk assessment, this paper compares and analyzes the performance of different machine learning algorithms in digital financial risk assessment to find the optimal model configuration, so as to improve the accuracy and timeliness of risk assessment. This paper firstly reviews the relevant literature systematically, and sorts out the current research progress in the field of digital financial risk assessment. Subsequently, the source of data collection and pre-processing steps are introduced in detail to ensure the quality and availability of data. In the feature engineering stage, through the in-depth mining and conversion of the original data, the feature variables that have a key impact on the digital financial risk assessment are extracted, a variety of mainstream algorithms are compared, and the model parameters are optimized by cross-validation and other methods. Experimental results show that the selected algorithm has good predictive performance on specific data sets, and support vector machine (SVM) and random forest algorithm are particularly outstanding on multiple evaluation indicators.